def test_loss_gradient_batch_mode(self, art_warning, batch_mode,
audio_data):
try:
import tensorflow.compat.v1 as tf1
test_input = audio_data
test_target = np.array(["This", "is", "a dummy", "a dummy"])
lingvo = TensorFlowLingvoASR()
if batch_mode:
gradients = lingvo._loss_gradient_per_batch(
test_input, test_target)
else:
gradients = lingvo._loss_gradient_per_sequence(
test_input, test_target)
gradients_abs_sum = np.array([np.abs(g).sum() for g in gradients],
dtype=object)
# test shape, equal inputs have equal gradients, non-zero inputs have non-zero gradient sums
assert [x.shape
for x in test_input] == [g.shape for g in gradients]
assert_allclose(np.abs(gradients[2]).sum(),
np.abs(gradients[3]).sum(),
rtol=1e-01)
assert_array_equal(gradients_abs_sum > 0,
[False, True, True, True])
except ARTTestException as e:
art_warning(e)
def test_loss_gradient(self, art_warning):
try:
transcripts = list()
audios = list()
for filename, sample in self.samples.items():
file_path = get_file(filename, sample["uri"])
_, audio = read(file_path)
audios.append(audio)
transcripts.append(sample["transcript"])
audio_batch = np.array(audios, dtype=object)
target_batch = np.array(transcripts)
lingvo = TensorFlowLingvoASR()
gradient_batch = lingvo._loss_gradient_per_batch(
audio_batch, target_batch)
gradient_sequence = lingvo._loss_gradient_per_sequence(
audio_batch, target_batch)
gradient_batch_sum = np.array(
[np.abs(gb).sum() for gb in gradient_batch], dtype=object)
gradient_sequence_sum = np.array(
[np.abs(gs).sum() for gs in gradient_sequence], dtype=object)
# test loss gradients per batch and per sequence are the same
assert_allclose(gradient_sequence_sum,
gradient_batch_sum,
rtol=1e-05)
# test gradient_batch, gradient_sequence and audios items have same shapes
assert ([gb.shape for gb in gradient_batch] ==
[gs.shape
for gs in gradient_sequence] == [a.shape for a in audios])
except ARTTestException as e:
art_warning(e)